Novel Method for Predicting Mercury Injection Capillary Pressure Curves of Tight Sandstone Reservoirs Using NMR T2 Distributions

In this research, mercury injection capillary pressure (MICP) experiments and nuclear magnetic resonance (NMR) experiments were carried out on 19 reservoir core samples. Based on the experimental results, the relationships between the capillary pressure (Pc) and NMR transverse relaxation time (T2) were established. A novel classified piecewise multi-parameter power function transformation (CPMPFT) method is proposed to predict the MICP curves using the NMR T2 distributions and is described as follows. First, the multi-parameter power function transformation model is established, after which the reservoir is classified and the appropriate pore segmentation point is selected. Then, for each type of reservoir, the coefficients of the transformation model for large and small pores are calculated based on the simulated annealing and genetic algorithm combined with non-linear programming, and the models for predicting the MICP curves are ultimately obtained. Using these models, the MICP curves can be predicted continuously based on the NMR T2 distribution, based on which the pore structure parameters, including the displacement pressure (Pd), median pressure (Pc50), and so forth, can be precisely extracted. 19 core samples and well data were processed using the proposed method, and the predicted results were compared with the measured results; the results were found to match well, thereby proving the reliability of the proposed method.